Aiming at the problems of aimless search, slow convergence and unsmooth path planning of ant colony algorithm in global path planning, this paper proposes a smooth path planning method that combines A ∗ ant colony and dynamic window algorithm. First, for the traditional ant colony algorithm, the improved A ∗ algorithm is used to distribute initial pheromones unevenly to solve the aimless problem of initial search of the algorithm. The self-defined moving step size and searching method are given to improve the efficiency of path optimization. The heuristic function value in the transition probability function is modified and the obstacle influence factor is added to avoid deadlock and speed up the convergence. The secondary path optimization strategy is adopted to make the path shorter and smoother. Secondly, the dynamic obstacle avoidance evaluation sub function is introduced into the evaluation function of the dynamic window method to improve the path safety. The simulation results show that the improved A ∗ ant colony algorithm can reduce the path length by 8. 75% and the turning points by 59% compared with the traditional ant colony algorithm. After the dynamic window method is integrated and optimized, the mobile robot not only ensures the global optimal path planning in the static environment, but also realizes the path planning in the dynamic environment, effectively avoids dynamic obstacles in the environment.